Hydroclimatology of the Chitral River in the Indus Basin under Changing Climate

Abstract

Biased distribution of hydro-climate stations in high elevations are major obstacles for reliable appraisal of the hydro-climatic regime of the Chitral Basin located in the extreme north of Pakistan. We modeled this regime in the ARC-SWAT hydrological model forced with the latest gridded reanalysis ERA5 Land dataset, bias-corrected against a good quality reference dataset. The performance of the gridded dataset was cross-validated by comparing the model flow simulation against the observed flows. The ERA5 Land overall provided reasonably good estimates. The calibrated model on the daily time scale was able to provide excellent values of the employed statistical measures (NSE, KGE, PBIAS, RMSE and MAE). For a future climate change analysis, climate series was devised using two future projection scenarios (RCP4.5 and RCP8.5) using the best performing GCM (MIROC5_rlilp1) out of five investigated GCMs. The results of the climate change analysis reveal increment in the average temperature up to +3.73 °C and +5.62 °C for RCP4.5 and RCP8.5, respectively, while the analysis of precipitation suggests an annual decrease up to −16% and −35% against RCP4.5 and RCP8.5, respectively, by the end of century. A future simulated flow analysis showed an increment of +0.25% and decrease of −6.82% for RCP4.5 and RCP8.5, respectively. Further analysis of climate suggests seasonal deflections especially in precipitation and flow regimes. A notable climb in flow quantities was observed during spring season (MAM) in spite of the major reduction in precipitation amounts for that season. This implicitly supports a high rate of glacial/snow melt especially in the spring season during that period. Frequent droughts and floods are also projected by examining flow durations at each interval of the 21st century.

Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections

“…Hence, the existing stations inadequately capture the precipitation amounts and patterns. Precipitation estimates from the ERA5-Land gridded dataset were reasonable for the UIB 26 , 43 , 47 . The ERA5-Land data are newly developed, available from 1981 to the near real-time with several weeks delay with an hourly temporal resolution, 0.1° × 0.1° spatial resolution and with a global spatial coverage 48 .…”

“…The station data can be helpful for bias correction, but as mentioned previously, the Hunza basin is data-scarce. So the global precipitation data of ERA5-Land, with its latest release and fine resolution 3 , 43 , 47 , were used for GCM bias-correction in this study. The ERA5-Land precipitation data, however, overestimate the precipitation and need to be corrected before being applied as the observed precipitation data for the Hunza basin.…”

A novel approach for evaluation of CMIP6 GCMs in simulating temperature and precipitation extremes of Pakistan

“…Heatwaves, droughts, floods and storms are some of the climatic extremes to which Pakistan is particularly vulnerable (Khan et al, 2020b). These kinds of disasters often wreak havoc on whole neighbourhoods, resulting in fatalities, evictions, property damage, and interruptions to lifelines like water and electricity (Bhatti et al, 2020;Khan et al, 2020aKhan et al, , 2023Syed et al, 2022). Those already disadvantaged, such as the poor and those who live in rural regions, are hit the hardest because of their lack of resources and resilience.…”

Evaluating Future Streamflow Patterns under SSP245 Scenarios: Insights from CMIP6

“…The runoff in the Upper Indus Basin depends on a host of factors, including the melting of seasonal snowpack, glacier melting, and precipitation [31]. The erratic changes in maximum and mean temperature during summer and especially in winter have intensified the glacier melting and consequently river flow, adding to the woes of the indigenous community living in the Upper Indus Basin [32]. Mountain ecosystems are widely acknowledged to be the most sensitive to climate change [33].…”